The supracrestal tissue attachment (SCTA) is the new terminology for biologic width. SCTA is defined as the physiologic dimension of a solitary functional unit composed of junctional epithelium and connective tissue attachment. Its preservation is critical for the well-being of periodontal health. SCTA has been widely studied and scientific literature is indicative of its significance during the placement of restoration, including prosthetic crowns. This should be taken care of in cases of anterior teeth within the smile zone, where dental crowns are regularly placed subgingivally for aesthetic reasons. In addition, any violation of SCTA while restoring the dentition will present as gingival inflammation and pain, consequently, leading to failure of the clinical procedure.

There is a paucity of information about molecularly driven therapy in osteosarcomas. We report a 31-year-old woman with chemotherapy-refractory metastatic osteosarcoma who was successfully treated with the combination of palbociclib (CDK4/6 inhibitor) and lenvatinib (multikinase FGFR inhibitor), selected based on next generation sequencing that showed CDK4 and CCND2 amplifications (upregulates CDK4/6), and FGF6 (ligand for FGFR1,2 and 4), FGF23 (ligand for FGFR1,2,3, and 4) and FRS2 (adaptor protein for FGFR signaling) amplifications. The patient\'s tumor showed 68% reduction in positron emission tomography (PET) avidity, lasting 31 months after therapy initiation, when a solitary recurrence occurred, was resected, and treatment continued. The patient remains on matched targeted therapy at 51 + months from the start of the combination. Treatment was given at reduced dosing (lenvatinib 10 mg oral daily (approved dose = 24 mg daily)) and palbociclib 75 mg oral daily, one week on and one week off (approved dose = 125 mg oral daily, three weeks on/one week off) and is tolerated well. Therefore, co-targeting the aberrant cyclin and FGFR pathways resulted in long-term exceptional response in a patient with refractory advanced osteosarcoma.

Spatial capture-recapture (SCR) models are powerful analytical tools that have become the standard for estimating abundance and density of wild animal populations. When sampling populations to implement SCR, the number of unique individuals detected, total recaptures, and unique spatial relocations can be highly variable. These sample sizes influence the precision and accuracy of model parameter estimates. Testing the performance of SCR models with sparse empirical data sets typical of low-density, wide-ranging species can inform the threshold at which a more integrated modeling approach with additional data sources or additional years of monitoring may be required to achieve reliable, precise parameter estimates. Using a multi-site, multi-year Utah black bear (Ursus americanus) capture-recapture data set, we evaluated factors influencing the uncertainty of SCR structural parameter estimates, specifically density, detection, and the spatial scale parameter, sigma. We also provided some of the first SCR density estimates for Utah black bear populations, which ranged from 3.85 to 74.33 bears/100 km2 . Increasing total detections decreased the uncertainty of density estimates, whereas an increasing number of total recaptures and individuals with recaptures decreased the uncertainty of detection and sigma estimates, respectively. In most cases, multiple years of data were required for precise density estimates (<0.2 coefficient of variation [CV]). Across study areas there was an average decline in CV of 0.07 with the addition of another year of data. One sampled population with very high estimated bear density had an atypically low number of spatial recaptures relative to total recaptures, apparently inflating density estimates. A complementary simulation study used to assess estimate bias suggested that when <30% of recaptured individuals were spatially recaptured, density estimates were unreliable and ranged widely, in some cases to >3 times the simulated density. Additional research could evaluate these requirements for other density scenarios. Large numbers of individuals detected, numbers of spatial recaptures, and precision alone may not be sufficient indicators of parameter estimate reliability. We provide an evaluation of simple summary statistics of capture-recapture data sets that can provide an early signal of the need to alter sampling design or collect auxiliary data before model implementation to improve estimate precision and accuracy.

UNASSIGNED: Stability of risk estimates from prediction models may be highly dependent on the sample size of the dataset available for model derivation. In this paper, we evaluate the stability of cardiovascular disease risk scores for individual patients when using different sample sizes for model derivation; such sample sizes include those similar to models recommended in the national guidelines, and those based on recently published sample size formula for prediction models.
UNASSIGNED: We mimicked the process of sampling N patients from a population to develop a risk prediction model by sampling patients from the Clinical Practice Research Datalink. A cardiovascular disease risk prediction model was developed on this sample and used to generate risk scores for an independent cohort of patients. This process was repeated 1000 times, giving a distribution of risks for each patient. N = 100,000, 50,000, 10,000, N min (derived from sample size formula) and N epv10 (meets 10 events per predictor rule) were considered. The 5-95th percentile range of risks across these models was used to evaluate instability. Patients were grouped by a risk derived from a model developed on the entire population (population-derived risk) to summarise results.
UNASSIGNED: For a sample size of 100,000, the median 5-95th percentile range of risks for patients across the 1000 models was 0.77%, 1.60%, 2.42% and 3.22% for patients with population-derived risks of 4-5%, 9-10%, 14-15% and 19-20% respectively; for N = 10,000, it was 2.49%, 5.23%, 7.92% and 10.59%, and for N using the formula-derived sample size, it was 6.79%, 14.41%, 21.89% and 29.21%. Restricting this analysis to models with high discrimination, good calibration or small mean absolute prediction error reduced the percentile range, but high levels of instability remained.
UNASSIGNED: Widely used cardiovascular disease risk prediction models suffer from high levels of instability induced by sampling variation. Many models will also suffer from overfitting (a closely linked concept), but at acceptable levels of overfitting, there may still be high levels of instability in individual risk. Stability of risk estimates should be a criterion when determining the minimum sample size to develop models.

BACKGROUND: Oncocytic carcinoma of the thyroid is a rare disease, characterized by a poor prognosis and low response rate to radioiodine therapy. Crizotinib is a specific anaplastic lymphoma kinase (ALK) inhibitor, which was initially developed in non-small cell lung cancer. Other solid tumors harboring a translocation in ALK have been described, such as renal carcinoma, thyroid, colorectal, ovarian cancers, and spitzoid melanoma. The research of ALK rearrangements in thyroid tumor is a promising therapeutic track, and treatments need to be explored.
METHODS: We report the case of a 76-year-old woman with a history of multinodular goiter, who was hospitalized for impairment of her general condition. She was diagnosed with metastatic oncocytic thyroid cancer. Synchrone metastases were found: Multiple mediastinal lymphadenopathies, lytic bone lesions and bilateral mammary lumps. Fluorescence in situ hybridization analysis revealed an ALK rearrangement in 61% of cells. No other mutation was found. A tumor board discussion based on molecular characteristics of the tumor suggested initiating a daily treatment by crizotinib, a specific ALK inhibitor. A positron emission tomography scan performed 4 mo after the initiation of crizotinib showed a complete metabolic response.
CONCLUSIONS: This case highlights an unexpected efficacy of crizotinib in an ALK-rearranged thyroid tumor, and the need of further assessments.

OBJECTIVE: The objective of this case study is to present the manufacturing process and delivery of a removable partial denture (RPD) using computer-aided design/computer-aided manufacturing (CAD/CAM) technology.
METHODS: A 46-year-old female patient presented at the Federal University of Rio Grande do Norte complaining of the lack of retention in her mandibular RPD. After the design and preparation of the abutment teeth, further appointments were made. In the first appointment, intraoral scanning (IOS) with a Trios scanner was performed, followed by virtual planning of the framework. The RPD was then printed, invested, cast by induction, and polished. In the second appointment, the RPD was delivered.
CONCLUSIONS: The technique proved to be efficient in terms of clinical time and sufficient for patient comfort and satisfaction.

Portable light-scattering aerosol monitors (PLSAMs) can supplement existing air quality monitoring networks through measuring air pollutant exposure concentrations at high spatiotemporal resolution. However, data collected by PLSAMs are often subject to the simplicity of measurement principle which may lead to errors compared to the regulatory data observed at fixed-site air quality monitoring stations. The main objective of this study was to develop a feasible experimental framework to assess the influence of key factors (e.g., relative humidity (RH)) on the performance of PLSAMs in the real-world conditions. Following the proposed framework, the accuracy and precision of the TSI DustTrak aerosol monitor were evaluated through side-by-side comparison with the stationary reference instruments (SRIs) while taking characteristics of particles, RH, and the concentration range into consideration. DustTrak generally demonstrated low accuracy but high precision in measuring PM2.5 concentrations at the two selected stations. Three calibration models between DustTrak and the SRIs were used to bias correct the DustTrak PM2.5 measurements. The RH-adjusted linear regression calibration method led to better calibration results than the simple linear regression method and the RH-adjusted empirical method, with CV R2 values higher than 0.97, root mean square error less than 1.0 μg/m3, and accuracy values at 3% for two DustTraks. The proposed experimental framework can be extended to field calibration of various types of PLSAMs, and the obtained calibration results can promote a more accurate investigation of particle air pollution using these PLSAMs.

BACKGROUND: Evidence synthesis reviews in health care rely on the efficient identification of research evidence, particularly evidence from randomised controlled trials (RCTs). There are no recently validated filters to identify RCTs in the Cumulative Index to Nursing and Allied Health Literature (CINAHL Plus).
OBJECTIVE: To develop, test and validate a search filter to identify reports of RCTs from CINAHL Plus.
METHODS: Nine sets of relevant and irrelevant records were identified to develop and test search filters iteratively. Two sets were used to validate the sensitivity and precision of the filters. The performance of two previously published filters and the filter built into EBSCOhost was evaluated.
RESULTS: We present a validated filter which offers sensitivity of 0.88 (95% CI: 0.77-0.95) and precision of 0.36 (95% CI: 0.31-0.41). This is comparable to the sensitivity of published filters, but has much better precision.
CONCLUSIONS: A sensitive and precise filter, developed using records selected based on title and abstract information, is available for identifying reports of RCTs in the CINAHL Plus database via EBSCOhost. Using this filter is likely to reduce the number of results needing to be screened to a quarter of those retrieved by other published filters.

The Nix Pro Colour Sensor™ (NIX) can be potentially used to measure meat colour, but procedural guidelines that assure measurement reproducibility and repeatability (precision) must first be established. Technical replicate number (r) will minimise response variation, measureable as standard error of predicted mean (SEM), and contribute to improved precision. Consequently, we aimed to explore the effects of r on NIX precision when measuring aged beef colour (colorimetrics; L*, a*, b*, hue and chroma values). Each colorimetric SEM declined with increasing r to indicate improved precision and followed a diminishing rate of improvement that allowed us to recommend r=7 for meat colour studies using the NIX. This definition was based on practical limitations and a* variability, as additional r would be required if other colorimetrics or advanced levels of precision are necessary. Beef ageing and display period, holding temperature, loin and sampled portion were also found to contribute to colorimetric variation, but were incorporated within our definition of r.

OBJECTIVE: The current study was performed to assess the precision of the principal subvisible particle measurement methods available today. Special attention was given to identifying the sources of error and the factors governing analytical performance.
METHODS: The performance of individual techniques was evaluated using a commercial biologic drug product in a prefilled syringe container. In control experiments, latex spheres were used as standards and instrument calibration suspensions.
RESULTS: The results reported in this manuscript clearly demonstrated that the particle measurement techniques operating in the submicrometer range have much lower precision than the micrometer size-range methods. It was established that the main factor governing the relatively poor precision of submicrometer methods in general and inherently, is their low sampling volume and the corresponding large extrapolation factors for calculating final results.
CONCLUSIONS: The variety of new methods for submicrometer particle analysis may in the future support product characterization; however, the performance of the existing methods does not yet allow for their use in routine practice and quality control.